IP Library › Granted Patent US 12,194,506
Granted Patent B2
US 12,194,506 · App. 17/752,669 · Granted Jan 14, 2025

Sorting of contaminants

Inventors: Nalin Kumar (Fort Worth, TX); Manuel Gerardo Garcia, Jr. (Fort Wayne, IN)
Assignee: SORTERA TECHNOLOGIES, INC.
B07C5/3422B07C5/34B07C5/342B07C5/04B07C2501/0054
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Quick Facts
Patent No.
US 12,194,506
App. No.
17/752,669
Filed
May 24, 2022
Granted
Jan 14, 2025
Kind
B2
Art Unit
3653
USPC
209/577
Abstract

A material sorting system sorts materials utilizing a vision system that implements a machine learning system in order to identify or classify each of the materials, which are then sorted into separate groups based on such an identification or classification. The material sorting system can sort material pieces containing contaminants, such as copper from steel.

Claims (49)

1. A system for classifying a first mixture of material pieces composed of ferrous metals, the system comprising:

a sensor configured to capture one or more characteristics of the first mixture of material pieces composed of ferrous metals; and

a data processing system comprising an artificial intelligence (“AI”) system configured to classify one or more of the material pieces composed of ferrous metals as containing a tramp element based on the one or more captured characteristics of the first mixture of material pieces composed of ferrous metals, wherein the classifying of one or more of the material pieces composed of ferrous metals as containing a tramp element is based on a knowledge base containing a previously generated library of observed characteristics captured from samples of material pieces containing the tramp element, wherein the sensor is a camera, and wherein the library of observed characteristics was captured by the camera configured to capture images of the samples of the material pieces containing the tramp element as they were conveyed past the camera, wherein the camera is configured to capture visual images of the first mixture of material pieces to produce image data, and wherein the observed characteristics are visually observed characteristics.

2. The system as recited in claim 1 , further comprising:

a conveyor system configured to convey the first mixture past the sensor; and

a sorter configured to sort the one or more classified material pieces from the first mixture as a function of the classifying of the one or more of the material pieces composed of ferrous metals as containing a tramp element.

3. A system for classifying a first mixture of material pieces composed of ferrous metals, the system comprising:

a sensor configured to capture one or more characteristics of the first mixture of material pieces composed of ferrous metals;

a data processing system comprising an artificial intelligence (“AI”) system configured to classify one or more of the material pieces composed of ferrous metals as containing a tramp element based on the one or more captured characteristics of the first mixture of material pieces composed of ferrous metals;

a conveyor system configured to convey the first mixture past the sensor; and

a sorter configured to sort the one or more classified material pieces from the first mixture as a function of the classifying of the one or more of the material pieces composed of ferrous metals as containing a tramp element, wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein the second mixture of material pieces contains an aggregate amount of the tramp element of less than 1 wt %.

4. The system as recited in claim 2 , wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein the second mixture of material pieces contains an aggregate amount of the tramp element of less than 0.05 wt %.

5. The system as recited in claim 1 , wherein the tramp element is copper.

6. The system as recited in claim 1 , wherein the tramp element is embedded within the material piece.

7. A method for classifying a first mixture of materials, the method comprising:

capturing a characteristic of the first mixture of materials with a sensor; and

assigning with an AI system a classification to certain ones of the first mixture of materials as containing a contaminant based on the captured characteristics characteristic of the first mixture of materials, wherein the classification is based on a knowledge base containing a previously generated library of one or more observed characteristics captured from a set of samples of materials containing the contaminant, wherein the library of observed characteristics was captured by a camera configured to capture visual images of the set of samples of the materials containing the contaminant as they were conveyed past the camera.

8. The method as recited in claim 7 , wherein the first mixture of materials is composed of ferrous metals, and wherein the contaminant is copper.

9. A method for classifying a first mixture of materials, the method comprising:

capturing a characteristic of the first mixture of materials with a sensor, wherein the first mixture of materials is composed of ferrous metals, and wherein the contaminant is copper;

assigning with an AI system a classification to certain ones of the first mixture of materials as containing a contaminant based on the captured characteristic of the first mixture of materials; and

sorting the certain ones of the first mixture of materials from the first mixture as a function of the classification, wherein the sorting produces a second mixture of materials that comprises the first mixture of materials minus the sorted certain ones of the first mixture of materials, wherein the second mixture of materials contains an aggregate amount of copper of less than 1 wt %.

10. The method as recited in claim 8 , further comprising sorting the certain ones of the first mixture of materials from the first mixture as a function of the classification, wherein the sorting produces a second mixture of materials that comprises the first mixture of materials minus the sorted certain ones of the first mixture of materials, wherein the second mixture of materials contains an aggregate amount of copper of less than 0.05 wt %.

11. A method for classifying a first mixture of materials, the method comprising:

capturing a characteristic of the first mixture of materials with a sensor; and

assigning with an AI system a classification to certain ones of the first mixture of materials as containing a contaminant based on the captured characteristic of the first mixture of materials, wherein the first mixture of materials is composed of plastics, and wherein the contaminant is a specified additive.

12. A computer program product stored on a computer readable storage medium, which when executed by a data processing system, performs a process comprising:

assigning with an AI system a classification to certain ones of a first mixture of material pieces as containing a contaminant based on one or more characteristics of the first mixture of material pieces captured with a sensor, wherein the classification is based on a knowledge base containing a previously generated library of one or more observed characteristics captured from a set of samples of material pieces containing the contaminant, wherein the library of observed characteristics was captured by a camera configured to capture visual images of the set of samples of the material pieces containing the contaminant as they were conveyed past the camera; and

sending instructions to a sorting device to sort the certain ones of the first mixture of material pieces from the first mixture, wherein the sorting is performed as a function of the classification.

13. The computer program product as recited in claim 12 , wherein the first mixture of material pieces is composed of ferrous metal scrap pieces, and wherein the contaminant is a tramp element.

14. The computer program product as recited in claim 13 , wherein the tramp element is embedded within one or more of the ferrous scrap pieces.

15. A computer program product stored on a computer readable storage medium, which when executed by a data processing system, performs a process comprising:

assigning with an AI system a classification to certain ones of a first mixture of material pieces as containing a contaminant based on one or more characteristics of the first mixture of material pieces captured with a sensor, wherein the first mixture of material pieces is composed of plastics, and wherein the contaminant is a specified additive.

16. The system as recited in claim 2 , wherein the first mixture of material pieces comprises ferrous metals containing the tramp element and ferrous metals not containing the tramp element, wherein the ferrous metals containing the tramp element are sorted from the first mixture of material pieces, wherein the ferrous metals containing the tramp element have a different chemical composition than the ferrous metals not containing the tramp element.

17. The method as recited in claim 8 , wherein the first mixture of materials comprises ferrous metals containing the contaminant and ferrous metals not containing the contaminant, wherein the ferrous metals containing the contaminant are sorted from the first mixture of materials, wherein the ferrous metals containing the contaminant have a different chemical composition than the ferrous metals not containing the contaminant.

18. The computer program product as recited in claim 13 , wherein the first mixture of material pieces comprises ferrous metal scrap pieces containing the tramp element and ferrous metal scrap pieces not containing the tramp element, wherein the ferrous metal scrap pieces containing the tramp element are sorted from the first mixture of material pieces, wherein the ferrous metal scrap pieces containing the tramp element have a different chemical composition than the ferrous metal scrap pieces not containing the tramp element.

19. The system as recited in claim 2 , wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein the second mixture of material pieces contains an aggregate amount of the tramp element of less than 1 wt %.

20. The method as recited in claim 8 , further comprising sorting the certain ones of the first mixture of materials from the first mixture as a function of the classification, wherein the sorting produces a second mixture of materials that comprises the first mixture of materials minus the sorted certain ones of the first mixture of materials, wherein the second mixture of materials contains an aggregate amount of copper of less than 1 wt %.

21. The method as recited in claim 7 , wherein the first mixture of materials is composed of plastics, and wherein the contaminant is a specified additive.

22. The computer program product as recited in claim 12 , wherein the first mixture of material pieces is composed of plastics, and wherein the contaminant is a specified additive.

23. The system as recited in claim 2 , wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein one or more parameters within the AI system are configured to classify one or more of the material pieces so that the second mixture of material pieces contains an aggregate amount of the tramp element of less than 1 wt %, wherein the tramp element is copper.

24. The system as recited in claim 2 , wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein one or more parameters within the AI system are configured to classify one or more of the material pieces so that the second mixture of material pieces contains an aggregate amount of the tramp element of less than 0.05 wt %, wherein the tramp element is copper.

25. The method as recited in claim 8 , wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein one or more parameters within the AI system are configured to classify one or more of the material pieces so that the second mixture of material pieces contains an aggregate amount of copper of less than 1 wt %.

26. The method as recited in claim 8 , wherein the sorting by the sorter of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein one or more parameters within the AI system are configured to classify one or more of the material pieces so that the second mixture of material pieces contains an aggregate amount of copper of less than 0.05 wt %.

27. The computer program product as recited in claim 12 , wherein the sorting by the sorting device of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein one or more parameters within the AI system are configured to classify the material pieces so that the second mixture of material pieces contains an aggregate amount of the contaminant of less than 1 wt %.

28. The computer program product as recited in claim 12 , wherein the sorting by the sorting device of the classified material pieces from the first mixture produces a second mixture of material pieces that comprises the first mixture minus the classified material pieces, wherein one or more parameters within the AI system are configured to classify the material pieces so that the second mixture of material pieces contains an aggregate amount of the contaminant of less than 0.05 wt %.

29. The system as recited in claim 1 , wherein the classification is based solely on the visually observed characteristics.

30. The method as recited in claim 7 , wherein the sensor is a camera, wherein the captured characteristic is a visually observed characteristic, and wherein the classification is based solely on the visually observed characteristic.

31. The computer program product as recited in claim 12 , wherein the sensor is a camera, wherein the captured characteristics are visually observed characteristics, and wherein the classification is based solely on the visually observed characteristics.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 27, 2024
From: KUMAR, NALIN; GARCIA, MANUEL GERARDO, JR.
To: SORTERA ALLOYS, INC.
Reel/Frame 069427/0867 →
CHANGE OF NAME Recorded Jul 19, 2023
From: SORTERA ALLOYS, INC.
To: SORTERA TECHNOLOGIES, INC.
Reel/Frame 064414/0101 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 25, 2022
From: KUMAR, NALIN; GARCIA, MANUEL GERARDO, JR.
To: SORTERA ALLOYS, INC.
Reel/Frame 060019/0092 →
Continuity (16)
Continuation In Part 17667397 · Feb 8, 2022
Continuation In Part 17495291 · Oct 6, 2021
Continuation In Part 17491415 · Sep 30, 2021
Continuation In Part 17380928 · Jul 20, 2021
Continuation In Part 17227245 · Apr 9, 2021
Continuation In Part 16939011 · Jul 26, 2020
Continuation In Part 16852514 · Apr 19, 2020
Continuation 16375675 · Apr 4, 2019
Division 16358374 · Mar 19, 2019
Continuation In Part 15963755 · Apr 26, 2018
Continuation In Part 15963755 · Apr 26, 2018
Continuation In Part 15213129 · Jul 18, 2016
Provisional Application 63193379 · May 26, 2021
Provisional Application 62490219 · Apr 26, 2017
Provisional Application 62193332 · Jul 16, 2015
Related Publication 20220355342A1 · Nov 10, 2022
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